A. Field of the Invention
This invention generally relates to methods and systems for developing optimized schedules and, more particularly, to methods and systems employed to develop optimized schedules in the transportation field, such as the commercial airline industry.
B. Description of the Related Art
Schedule optimization is the process of selecting an arrangement of resources to maximize a desired benefit. In the airline industry, this process involves the selection and arrangement of flights (or legs) into a schedule that maximizes airline profit.
FIG. 1 depicts the process currently employed for schedule optimization in the airline industry. Underlying the process, one or more schedulers 10 utilize a computer system to selectively run a pair of conventional applications known in the art as the airline profitability model (APM) and the fleet assignment model (FAM).
During initialization, the APM receives a current schedule 12 of flights, logit parameters 14, and marketing data 16. The current schedule 12 typically includes the arrival and departure times, and the equipment assignments for all flights of the host airline (HA), the airline seeking schedule optimization. The current schedule 12 generally also includes similar information for all other airlines (OAs). The logit parameters 14 represent well-known estimates for the level of importance that the public may place on various aspects of a flight, such as whether a flight is non-stop. The marketing data 16 typically includes the total demand for flights in various markets.
Thereafter, the APM performs a conventional base run 18 on the input data to produce APM data 20. Among the APM data 20 produced are cost, demand, and revenue estimates for the current schedule 12 on a per flight, per itinerary, or per market basis, as desired. As used herein, a flight is a non-stop service in between an origination and a destination (the pair defining a market), while an itinerary is one or more interconnected flights.
The scheduler 10 then reviews the APM data 20 to identify changes that may improve the HA's schedule. Such changes may include adding a flight, canceling a flight, shifting the departure time of a flight, altering the frequency of a flight, and the like. Assuming that a potentially desirable change is identified, the scheduler 10 creates a proposed schedule 22 by manually incorporating the change into the current schedule 12. This step is no small matter, for nearly any change to a schedule must be made under the inherent constraints imposed by the rest of the schedule. For example, adding a new flight may mean that an existing flight needs to be canceled, which could also impact other flights in the schedule.
Assuming that the scheduler 10 is able to incorporate the change, the scheduler 10 directs the APM to perform a conventional incremental run 24 on the proposed schedule 22. The APM data 26 output includes cost, demand, and revenue estimates for the proposed schedule 22. This output is then fed to a conventional fleet assignment model (FAM) 28, which produces a fleeted schedule 30 consistent with the APM data 26. The fleeted schedule 30 is then run through the APM to produce APM data 20 indicating cost, demand, and revenue based on the fleeting of the proposed schedule 22. At this point, the scheduler 10 compares the APM data 20 just produced with that generated from the original schedule 12 to see if the change incorporated into the proposed schedule 22 increased HA profit. Profitable changes are ultimately made part of a final schedule 32.
However, as is often the case, several iterations of this time-consuming process are required to confirm the discovery of even a single profitable change to the schedule. Another significant issue facing the scheduler 10 is that there are so many changes that could logically be considered for entry into the schedule. As such, it is desirable to be able to incorporate multiple changes into the proposed schedule 22 for consideration.
Unfortunately, as those skilled in the art appreciate, the nature of conventional APMs and FAMs limits the number of possible changes that may be considered at one time. Specifically, running an APM on a “heavily overbuilt schedule” (i.e., a schedule with several proposed changes incorporated therein) produces inaccurate demand estimates. Generally, this inaccuracy increases as the difference between the proposed schedule 22 and the fleeted schedule 30 becomes larger relative to the overall size of the fleeted schedule 30. Consequently, only small incremental changes to a proposed schedule 22 can be evaluated with any reasonable degree of accuracy. Moreover, those skilled in the art know that the FAM is similarly limited to fleeting proposed schedules 22 with relatively few incorporated changes.
In summary, the present process is time and resource inefficient based on the combined effects of having to: (1) manually identify and incorporate proposed changes into a schedule; and 2) limit to relatively few the number of proposed changes for testing in a single run of the APM and FAM models. There is therefore a need for a method and system to overcome these and other limitations of the prior art approach.